The prediction of transcription factor binding site (TFBS) is essential for under-standing the combination mechanism and cell function of transcription factors. Although there are already several algorithms to predict TFBS, there is still room for improvement in the prediction effect. This study proposes a new neural net-work model called DeepCatl, which combines DNA sequence and shape characteristics to improve the accuracy and reliability of TFBS prediction. DeepCatl uses the convolutional neural network (CNN), channel attention module, and the improved Transformer encoder to capture DNA characteristics with refinement and comprehensiveness. The DeepCatl model extracts the sequence features of DNA through convolutional neural networks and channel attention mechanism, and extracts the structural features of DNA through Transformer encoder and Bi-LSTM. In the end, based on the fusion of these characteristics, DeepCatl is calculated and predicts the location of TFBS. We verified the superior performance of the DeepCatl model on the 165 Encode Chip-seq dataset, and the results show a significant improvement over traditional methods. This deep learning model that combines the channel attention mechanism and the Transformer encoder provides a new approach for bioinformatics research, which can further study the complex mechanism of interaction with DNA. The DeepCatl model not only integrates the channel attention mechanism and transformer encoding, but also extracts features from DNA sequences and DNA structures to predict transcription factor binding sites (TFBS).